
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "33"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 93 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 89 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.151944	0.218284	11.477920	2.519147	10.777910	0.551296	12.653362	2.294079	0.7625	0.7652	0.2748	17.146091	13.002079
2459593	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.924242	2.445526	16.904178	3.201459	3.023753	0.289823	5.289753	0.411874	0.7095	0.7058	0.2707	4.378159	4.024636
2459592	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.291306	0.170896	2.944800	0.074019	0.747815	0.146640	3.213894	0.024095	0.0302	0.0303	0.0011	nan	nan
2459591	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.483093	2.965050	20.607650	4.482602	3.245329	0.830773	2.596191	0.191327	0.6512	0.6729	0.3825	3.709618	3.878826
2459590	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.185794	3.052437	18.408592	3.613589	3.372128	0.877446	2.378473	-0.807757	0.6548	0.6723	0.3774	5.844562	5.674372
2459589	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.570613	2.540845	11.558467	2.935652	22.332487	0.073287	7.130298	1.580199	0.6782	0.6820	0.3161	3.518437	3.506140
2459588	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.212280	2.550095	19.217110	4.777385	6.095127	0.767619	7.903383	0.988564	0.7568	0.7393	0.2349	11.909210	7.423709
2459587	dish_ok	100.00%	-	-	-	100.00%	0.00%	349.831480	351.265025	inf	inf	-1.598895	106.442026	-4.675000	474.991646	nan	nan	nan	0.000000	0.000000
2459586	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.523473	1.109866	1.586888	0.290623	0.670054	-0.643112	1.614915	-0.902473	0.6575	0.6690	0.3683	4.460485	4.249827
2459585	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.559866	1.692718	2.983431	-0.057382	2.138523	-0.307817	5.598517	0.325004	0.0337	0.0327	0.0010	0.000000	0.000000
2459584	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.987310	18.948390	7.323402	4.881692	22.446989	19.596019	0.041237	-0.685708	0.6134	0.6320	0.3758	4.727516	4.572101
2459583	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.269415	2.557180	6.112799	2.667887	-0.034476	0.145800	1.330948	-0.253530	0.6471	0.6643	0.3986	6.340438	5.714638
2459582	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.133442	2.452824	5.253152	2.416864	1.071673	0.367491	1.994304	-0.024954	0.6565	0.6715	0.3942	5.317457	5.093380
2459581	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459580	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.289488	2.607955	6.094887	3.097265	-0.066430	0.215375	0.548260	-1.036389	0.6618	0.6776	0.3908	6.784394	6.075934
2459579	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.317572	2.472272	7.135250	3.493763	0.629265	0.644339	3.477143	1.075199	0.6563	0.6729	0.3906	2.949248	3.114622
2459578	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.674853	1.763160	0.717042	0.772151	-0.255237	1.186568	1.156760	-0.738287	0.0308	0.0311	0.0006	nan	nan
2459577	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.611803	3.349131	5.757000	2.636611	2.043347	0.696129	-0.156585	-1.130765	0.6605	0.6802	0.3949	4.102224	4.076902
2459576	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.368828	2.381991	4.442228	1.682808	1.886426	0.785652	0.543594	-0.998914	0.6812	0.6959	0.3908	3.249402	3.183948
2459575	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.725225	2.280041	3.744517	1.687221	5.573407	-0.105201	5.778739	1.221108	0.7408	0.7416	0.3485	0.000000	0.000000
2459574	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.951927	2.242896	4.144707	1.464764	2.869639	0.245549	0.196938	-0.995822	0.6843	0.6991	0.3797	5.051267	4.749131
2459573	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.220299	2.659892	7.732329	3.988547	0.952275	0.690538	0.792125	-0.744871	0.6624	0.6816	0.4011	0.000000	0.000000
2459572	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.035089	2.334624	4.219773	1.700443	2.379531	0.115927	1.112210	-0.962629	0.6657	0.6833	0.4014	4.982391	4.871169
2459571	dish_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.949645	1.628263	0.475159	0.750560	2.598224	-0.172890	0.913218	-0.798588	0.0317	0.0318	0.0007	nan	nan
2459570	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.863930	0.731167	-1.358221	0.301158	-1.107788	0.140445	-0.841015	-1.043512	0.6433	0.6499	0.3629	5.843526	5.515649
2459569	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.060217	2.378393	4.932540	2.060464	6.045794	1.484451	5.671153	1.546185	0.7343	0.7439	0.2632	6.198062	5.297455
2459566	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.505063	2.460604	6.049768	2.611334	1.607553	0.544975	-0.149317	-1.006072	0.6682	0.6837	0.4068	0.000000	0.000000
2459565	dish_ok	0.00%	-	-	-	100.00%	0.00%	0.628464	0.628464	0.580466	0.580466	-0.687930	-0.687930	-0.798047	-0.798047	nan	nan	nan	0.000000	0.000000
2459564	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.136203	3.397176	-1.922473	2.866349	-2.065527	-0.184025	-1.193469	-1.782700	0.6867	0.6781	0.3993	3.787632	3.317848
2459563	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.808426	7.359330	14.112768	23.862884	5.771596	14.409410	-0.645587	-1.974285	0.6724	0.6607	0.4066	4.872632	4.512327
2459562	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.621471	2.147441	-1.458737	0.960047	-2.466700	-0.417950	-0.858153	-1.435183	0.6827	0.6763	0.3912	3.648766	3.286128
2459561	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.097214	2.810694	-0.888147	1.526901	-2.188042	0.051165	-0.355247	-1.724615	0.6837	0.6757	0.3873	4.670146	3.775867
2459560	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.294235	2.346442	-1.309855	1.322355	-1.889230	0.292604	-0.763037	-1.784927	0.6822	0.6732	0.4012	4.432962	3.725598
2459559	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.073394	3.353035	-0.822745	1.838115	-1.775677	0.661169	-0.746105	-1.323798	0.6814	0.6777	0.3948	3.570229	3.152094
2459558	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.935115	7.532864	7.653322	12.473699	5.833529	14.489825	-0.772302	-1.743274	0.6801	0.6696	0.4062	2.680297	2.640462
2459557	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0354	0.0323	0.0010	nan	nan
2459556	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.104022	8.022925	7.949252	12.906051	4.535104	13.038033	-1.972093	-3.327770	0.6875	0.6789	0.3929	2.753872	2.601245
2459554	dish_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6804	0.6680	0.4144	nan	nan
2459552	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.041573	8.981169	11.590306	17.974791	5.906443	20.570113	0.611531	0.182709	0.6837	0.6733	0.4091	5.695462	3.370898
2459551	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.160757	2.532008	-1.300250	1.671035	-3.020052	0.535806	-0.756101	-1.932216	0.6755	0.6676	0.4066	3.319682	3.022609
2459550	dish_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0305	0.0306	0.0010	nan	nan
2459549	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.791416	7.164297	7.402878	13.128741	5.571547	14.433053	-0.806524	-2.450315	0.6775	0.6699	0.4155	2.762229	2.647497
2459542	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.366616	7.588841	14.915950	26.279495	-0.332798	1.202300	-1.257180	-2.085413	0.7389	0.7252	0.3725	3.456158	3.297366
2459541	dish_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	3.488885	6.683336	9.620871	15.619124	5.658458	12.091134	2.905405	3.918354	0.7214	0.7062	0.3486	5.665241	5.150695
2459540	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.065586	3.748312	0.368244	2.768482	-2.678378	1.545419	-0.909223	-0.822206	0.7359	0.7127	0.3667	3.722529	3.130635
2459536	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.429125	11.638894	12.427193	21.885838	9.256560	21.127214	-2.089770	-4.451962	0.7126	0.7062	0.4244	9.083561	6.165562
2459535	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.849537	2.706481	0.041449	2.413594	-3.320428	1.647425	-1.061253	-1.473767	0.7669	0.7344	0.3856	3.246574	3.081692
2459534	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.195263	2.459953	-0.867782	1.555134	-2.156935	0.670153	-0.291380	-1.335206	0.7581	0.7341	0.3751	7.746409	7.133324
2459533	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.639203	8.153081	7.831968	13.341634	5.342919	13.507755	-0.249066	-1.604120	0.6980	0.6895	0.4049	2.713802	2.691462
2459532	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.045094	9.485516	10.320181	17.134161	5.675668	16.502388	-0.241650	-2.111021	0.6993	0.6880	0.4087	2.846954	2.774755
2459530	dish_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.006902	2.610641	-0.891286	1.374689	-2.815629	0.520101	-0.993330	-1.568307	0.7224	0.7087	0.4066	2.994668	2.917267
2459527	dish_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	dish_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	dish_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	dish_ok	100.00%	100.00%	100.00%	0.00%	-	-	3.089393	5.428409	2.798178	9.667618	2.040005	1.212285	1.885106	-0.339852	0.0370	0.0357	0.0011	nan	nan
2459508	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.065587	8.743543	6.806951	10.904707	5.913333	9.991138	6.752894	12.606910	0.8816	0.8980	0.2839	0.000000	0.000000
2459505	dish_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.670249	6.036046	8.580593	12.813832	1.476697	6.781876	-0.809522	-0.485766	0.8754	0.8328	0.2281	3.809950	3.372352

Antenna Season Report Notebook¶

Antenna 33 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 33, Node 2:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 33: 2459617

Antenna 33: 2459616

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Antenna 33: 2459613

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶